Sr AI Engineer II - Enterprise AI Platforms

American Express•Sunrise, FL

10h•$123,000 - $215,250

About The Position

American Express is building an enterprise-grade Agentic AI Platform that enables teams to safely build, deploy, operate, discover, govern, and continuously improve AI agents at scale. The Senior AI Engineer will help design and build foundational platform capabilities at the intersection of AI systems, distributed platforms, cloud infrastructure, developer tooling, and enterprise governance. The role focuses on platform and infrastructure that enables teams and agents to operate safely and reliably at enterprise scale, rather than on individual AI applications. The engineer will contribute to agent runtime and execution infrastructure, a unified control plane for agents across execution environments and providers, governance and security controls, agent discovery and registries, observability, evaluation and continuous learning, developer experience, and AI-focused CI/CD and platform engineering. The work includes building scalable services and abstractions, APIs, tooling, telemetry, evaluation pipelines, and deployment patterns while partnering with product, architecture, security, risk, infrastructure, and application engineering teams. The Enterprise Data & AI team creates the platforms, products, and governance that power enterprise AI, supporting use cases from inception through implementation. The platform is intended to make the secure and reliable path the easiest path, allowing developers to build and operate agents across AI ecosystems with governance, identity, observability, evaluation, deployment, and operational capabilities built in.

Requirements

Strong software engineering experience building production systems using languages such as Python, Java, Go, or TypeScript.
Strong understanding of Generative AI, LLMs, agent architectures, tool/function calling, retrieval, context management, and agent orchestration.
Experience building production applications or platforms using major model providers or AI platforms.
Experience with Kubernetes, containers, microservices, distributed systems, and cloud-native architecture.
Experience designing production APIs and event-driven or asynchronous systems.
Strong understanding of modern cloud infrastructure and infrastructure-as-code practices.
Experience with CI/CD, automated testing, production observability, and software delivery practices.
Strong understanding of security fundamentals, including identity, authentication, authorization, secrets, and least-privilege access.
Ability to navigate ambiguous technical problems and turn emerging technologies into reliable production systems.
Strong communication skills and ability to collaborate across engineering, architecture, product, security, and governance organizations.
Bachelor’s degree required.

Nice To Haves

Experience with agent frameworks and orchestration technologies such as LangGraph, Semantic Kernel, Google ADK, OpenAI Agents SDK, or similar frameworks.
Experience with Model Context Protocol (MCP) and emerging agent interoperability protocols such as A2A.
Experience with multi-agent systems and agent-to-agent communication patterns.
Experience with Kubernetes operators, controllers, service meshes, or sophisticated Kubernetes platform engineering.
Experience with agent/model gateways and intelligent model routing.
Experience with vector databases, retrieval systems, embeddings, and enterprise knowledge architectures.
Experience with LLM and agent evaluation frameworks, LLM-as-judge techniques, human feedback systems, experimentation, and quality measurement.
Experience with AI observability, distributed tracing, OpenTelemetry, Langfuse, and production monitoring.
Experience with Evals.
Experience with policy engines and policy-as-code.
Experience with AI security, prompt injection defenses, tool security, data-loss prevention, and AI-specific threat modeling.
Experience with platform engineering, internal developer platforms, developer portals, and enterprise service catalogs.
Experience with large-scale distributed systems operating in highly regulated environments.

Responsibilities

Design and implement highly scalable components of the enterprise Agentic AI Platform.
Own complex technical problems from architecture through production implementation and operation.
Build distributed services and APIs supporting agent execution and lifecycle management.
Develop integrations with major AI/model ecosystems and managed agent platforms.
Establish common abstractions across models, agents, tools, runtimes, and providers.
Design systems for agent identity, permissions, policy enforcement, and governance.
Build observability, evaluation, and experimentation capabilities for production AI systems.
Develop SDKs, tooling, and automation that improve developer productivity.
Design reliable CI/CD and release processes for AI agents and related artifacts.
Establish patterns for resilience, scalability, performance, security, and cost efficiency.
Evaluate emerging agent frameworks, protocols, models, and infrastructure technologies and determine their fit within the enterprise platform.
Partner with product, architecture, security, risk, infrastructure, and application engineering teams.
Design and build scalable agent runtime infrastructure using Kubernetes and cloud-native technologies.
Develop runtime abstractions for agents operating in internally managed sandboxed environments and third-party AI platforms.
Build capabilities for agent orchestration, tool execution, state and context management, memory, asynchronous workloads, event-driven execution, and multi-agent workflows.
Develop APIs and services for agent registration, configuration, deployment, versioning, lifecycle management, policy enforcement, and runtime management.
Build platform controls for authentication and authorization, tool permissions, data boundaries, auditability, and lifecycle controls.
Build registries and catalogs for discovering reusable agents, tools, skills, prompts, models, knowledge sources, and other platform capabilities.
Develop metadata, ownership, versioning, dependency, certification, and discovery mechanisms for the enterprise agent ecosystem.
Build end-to-end telemetry for agent execution, including traces, events, model interactions, tool calls, latency, token consumption, cost, failures, policy decisions, and quality signals.
Develop capabilities that make complex and multi-agent workflows explainable and debuggable.
Develop evaluation frameworks and automated pipelines for agent quality, reliability, safety, and task performance, incorporating offline evaluations, production signals, human feedback, regression testing, and experimentation.
Help create continuous learning loops that turn production telemetry and feedback into improvements to agents, prompts, tools, models, and platform capabilities.
Build SDKs, APIs, CLIs, templates, local development environments, and self-service workflows for agent development.
Create developer paved roads that incorporate enterprise security, governance, observability, and operational standards.
Build AI-focused CI/CD capabilities for evaluation, policy validation, security checks, artifact and version management, deployment, promotion, rollback, and release controls.
Design GitOps and infrastructure-as-code patterns for deploying and managing agent workloads.